Automatic Classification of Non-Functional Requirements From App Store Reviews. Reviewing and Applying Approaches From Current Research

Table of Contents

1 Introduction

1.1 Objectives of the Thesis

1.2 Structure of the Thesis

2 Theoretical Background

2.1 Introduction to Requirements Engineering

2.2 Distinction Between Functional and Non-Functional Requirements

3 Literature Review

3.1 Methodology

3.2 Different Taxonomies for Non-Functional Requirements

3.3 App Store Reviews Containing Non-Functional Requirements

3.4 Different Machine Learning Algorithms to Classify Non-Functional Requirements

4 Applying the Support Vector Machine Algorithm to Classify an Existing Dataset

5 Results

6 Discussion

6.1 Contributions to Theory

6.2 Contributions to Practice

6.3 Limitations and Future Work

7 Conclusion

Objectives and Thematic Focus

This thesis aims to explore the nature of non-functional requirements (NFRs) within app store reviews and evaluates the effectiveness of machine learning algorithms in automatically classifying them. By reviewing existing taxonomies and ML methodologies, the work demonstrates a practical application using a Support Vector Machine (SVM) classifier to identify and categorize these requirements, thereby supporting developers in creating user-centered software.

• Theoretical foundations of Requirements Engineering and NFRs
• Taxonomies and categorization schemes for non-functional requirements
• Characterization of app store reviews as a source for user requirements
• Evaluation of machine learning algorithms (specifically SVM) for automated text classification

Excerpt from the Thesis

Summary of Chapters

1 Introduction: Introduces the importance of Requirements Engineering in software development and outlines the research objectives regarding the automatic classification of NFRs.

2 Theoretical Background: Defines key concepts of Requirements Engineering and establishes the distinction between functional and non-functional requirements.

3 Literature Review: Provides an overview of NFR taxonomies and examines existing machine learning approaches used to extract and classify requirements from app store data.

4 Applying the Support Vector Machine Algorithm to Classify an Existing Dataset: Details the practical implementation of an SVM classifier using Python to process and categorize app store reviews.

5 Results: Presents and evaluates the performance metrics (accuracy, precision, recall, F-measure) of the developed machine learning models.

6 Discussion: Reflects on the theoretical and practical contributions of the thesis and addresses limitations regarding dataset size and manual labeling.

7 Conclusion: Summarizes the key findings and highlights the potential for future research in automated NFR classification.

Key Terms

Requirements Engineering, Non-Functional Requirements, App Store Reviews, Machine Learning, Support Vector Machine, Text Classification, User-Centered Design, Software Quality, Natural Language Processing, Supervised Learning, Performance Metrics, Automated Analysis

Frequently Asked Questions

What is the core focus of this research?

This research focuses on the automated classification of non-functional requirements (NFRs) extracted from user-generated app store reviews using machine learning techniques.

What are the primary themes covered?

The thesis covers the theoretical definition of NFRs, taxonomies in literature, characteristics of app store reviews, and the application of supervised machine learning algorithms to automate requirement identification.

What is the main objective of this study?

The primary objective is to investigate current research approaches and implement a Support Vector Machine (SVM) algorithm to classify NFRs from an existing dataset of mobile app reviews.

Which scientific method is employed?

The research conducts a comprehensive literature review followed by a technical implementation using Python (Scikit-learn) to train and test a Support Vector Classifier (SVC) for binary classification of NFRs.

What is covered in the main body?

The main body examines different NFR taxonomies, the nature of app store feedback, and the technical pipeline for developing an ML-based classification model, including data preprocessing and performance evaluation.

Which keywords characterize this work?

The work is characterized by terms such as Requirements Engineering, NFRs, Machine Learning, SVM, and App Store Reviews.

Why are Support Vector Machines chosen for the implementation?

The literature review identified that SVM performed consistently well in previous studies for text classification tasks, making it a suitable choice for this thesis.

What are the main limitations identified in the results?

The study highlights limitations such as a relatively small dataset, potential bias from manual labeling, and the issue of class imbalance when using independent binary classifiers.